Tuesday, February 21, 2017

A (fairly) recent paper from Coutanche, Solomon, & Thompson-Schill, "A meta-analysis of fMRI decoding: Quantifying influences on human visual population codes" (full citation below), has an interesting approach to the effort to understand the spatial scale at which information is present in fMRI signals.

Coutanche, Solomon, & Thompson-Schill 2016 describes a meta-analysis of visual MVPA studies, the details of which (and most findings) I won't get into here. But I do want to highlight their use of the different spatial resolutions (acquired voxel size) across studies to get at spatial resolution. In their words,

"Multi-voxel decoding should be optimized (all else being equal) when the voxel size of acquired data matches the spatial resolution (i.e., granularity) of a region's information-containing patterns. We hypothesized that if V1 holds a more fine-grained map of information than later visual regions, employing larger voxels should not benefit decoding in V1, but may benefit decoding in post-V1 regions (through greater signal-to-noise at the scale of these patterns). .... Naturally, at a certain point, increasing the voxel size is expected to impair performance for any region."

The "all else being equal" does a lot of work, since there are major interactions between acquisition parameters and the signal-to-noise in the resulting functional images (I'm far from convinced that using voxels around 2 mm isotropic or smaller is a good idea for general task fMRI, but that's another topic!). But if I take as a starting assumption that we have equally good signal-to-noise across a sensible range of voxel sizes, do I accept that decoding should then be optimized "when the voxel size of acquired data matches the spatial resolution (i.e., granularity) of a region's information-containing patterns"?

The idea that, "at a certain point, increasing the voxel size is expected to impair performance for any region", strikes me as plausible: if the voxels are large enough to encompass the entire region, only the average activity of the region as a whole can be used in the analysis, losing any information contained in within-region activation patterns. However, no brain region exists in a vacuum - they are surrounded by other brain structures - and fMRI voxels don't have sharply-defined edges, so in practice, too-large voxels will have signal from adjacent regions, and the combination of regions might have quite a lot of information.

Matching the voxel size to the spatial resolution might indeed optimize decoding if the brain was a fixed grid (so the voxels could be aligned to coincide perfectly with the grid), but I'm not convinced that it's a useful aim for actual fMRI datasets: even if both the voxels and spatial resolution was at 1 mm isotropic, the chance that the voxels would align with the brain grid seems vanishingly small. Setting the voxel size to something like 2 mm seems better, with the aim of having each voxel contain at least one of the 1 mm information units (in other words, setting the voxels larger than the true spatial resolution).

Overall, I accept that idea that voxel size could be used as a marker of spatial resolution in the abstract: a fixed (unmoving, not surrounded by other regions) region and equally good signal-to-noise across the range of voxel sizes. In actual fMRI datasets, I'm not as convinced that it's safe to infer information spatial resolution from voxel resolution, but it is an intriguing idea.